Pytorch中TensorDataset,DataLoader的联合使用方式

from torch.utils.data import TensorDataset,DataLoader
import torch
 
a = torch.tensor([[1, 2, 3],
                  [4, 5, 6],
                  [7, 8, 9],
                  [1, 2, 3],
                  [4, 5, 6],
                  [7, 8, 9],
                  [1, 2, 3],
                  [4, 5, 6],
                  [7, 8, 9],
                  [1, 2, 3],
                  [4, 5, 6],
                  [7, 8, 9]])
 
b = torch.tensor([44, 55, 66, 44, 55, 66, 44, 55, 66, 44, 55, 66])
train_ids = TensorDataset(a,b)
# 切片输出
print(train_ids[0:4]) # 第0,1,2,3行
# 循环取数据
for x_train,y_label in train_ids:
    print(x_train,y_label)

# 参数说明，dataset=train_ids表示需要封装的数据集，batch_size表示一次取几个
# shuffle表示乱序取数据，设为False表示顺序取数据，True表示乱序取数据
train_loader = DataLoader(dataset=train_ids,batch_size=4,shuffle=False)
# 注意enumerate返回值有两个,一个是序号，一个是数据（包含训练数据和标签）
for i,data in enumerate(train_loader,1):
    train_data, label = data
    print(' batch:{0} train_data:{1}  label: {2}'.fORMat(i+1, train_data, label))

class TensorDataset(Dataset[Tuple[Tensor, ...]]):
    r"""Dataset wrapping tensors.
    Each sample will be retrieved by indexing tensors along the first dimension.
    Arguments:
        *tensors (Tensor): tensors that have the same size of the first dimension.
    """
    tensors: Tuple[Tensor, ...]
 
    def __init__(self, *tensors: Tensor) -> None:
        assert all(tensors[0].size(0) == tensor.size(0) for tensor in tensors)
        self.tensors = tensors
 
    def __getitem__(self, index):
        return tuple(tensor[index] for tensor in self.tensors)
 
    def __len__(self):
        return self.tensors[0].size(0)
 
# 由于此类内容过多，故仅列举了与本文相关的参数，其余参数可以自行去查看源码
class DataLoader(Generic[T_co]):
    r"""
    Data loader. Combines a dataset and a sampler, and provides an iterable over
    the given dataset.
    The :class:`~torch.utils.data.DataLoader` supports both map-style and
    iterable-style datasets with single- or multi-process loading, customizing
    loading order and optional automatic batching (collation) and memory pinning.
    See :py:mod:`torch.utils.data` documentation page for more details.
    Arguments:
        dataset (Dataset): dataset from which to load the data.
        batch_size (int, optional): how many samples per batch to load
            (default: ``1``).
        shuffle (bool, optional): set to ``True`` to have the data reshuffled
            at every epoch (default: ``False``).
    """
    dataset: Dataset[T_co]
    batch_size: Optional[int]
 
    def __init__(self, dataset: Dataset[T_co], batch_size: Optional[int] = 1,
                 shuffle: bool = False):
 
        self.dataset = dataset
        self.batch_size = batch_size

class Dataset(object):
    r"""An abstract class representing a :class:`Dataset`.

    All datasets that represent a map from keys to data samples should subclass
    it. All subclasses should overwrite :meth:`__getitem__`, supporting fetching a
    data sample for a given key. Subclasses could also optionally overwrite
    :meth:`__len__`, which is expected to return the size of the dataset by many
    :class:`~torch.utils.data.Sampler` implementations and the default options
    of :class:`~torch.utils.data.DataLoader`.

    .. note::
      :class:`~torch.utils.data.DataLoader` by default constructs a index
      sampler that yields integral indices.  To make it work with a map-style
      dataset with non-integral indices/keys, a custom sampler must be provided.
    """

    def __getitem__(self, index):
        raise NotImplementedError

    def __add__(self, other):
        return ConcatDataset([self, other])

    # No `def __len__(self)` default?
    # See NOTE [ Lack of Default `__len__` in python Abstract Base Classes ]
    # in pytorch/torch/utils/data/sampler.py

import torch
from torch.utils.data import Dataset, DataLoader, TensorDataset
from torch.autograd import Variable
import numpy as np
import pandas as pd

value_df = pd.read_csv('data1.csv'）
value_array = np.array(value_df)
print("value_array.shape =", value_array.shape)  # (73700, 300)
value_size = value_array.shape[0]  # 73700
train_size = int(0.7*value_size)

train_array = val_array[:train_size]  
train_label_array = val_array[60:train_size+60]

class DealDataset(Dataset):
    """
        下载数据、初始化数据，都可以在这里完成
    """

    def __init__(self, *arrays):
        assert all(arrays[0].shape[0] == array.shape[0] for array in arrays)
        self.arrays = arrays

    def __getitem__(self, index):
        return tuple(array[index] for array in self.arrays)

    def __len__(self):
        return self.arrays[0].shape[0]


# 实例化这个类，然后我们就得到了Dataset类型的数据，记下来就将这个类传给DataLoader，就可以了。
train_dataset = DealDataset(train_array, train_label_array)

train_loader2 = DataLoader(dataset=train_dataset,
                           batch_size=32,
                           shuffle=True)

for epoch in range(2):
    for i, data in enumerate(train_loader2):
        # 将数据从 train_loader 中读出来,一次读取的样本数是32个
        inputs, labels = data

        # 将这些数据转换成Variable类型
        inputs, labels = Variable(inputs), Variable(labels)

        # 接下来就是跑模型的环节了，我们这里使用print来代替
        print("epoch：", epoch, "的第", i, "个inputs", inputs.data.size(), "labels", labels.data.size())

class TensorDataset(Dataset):
    r"""Dataset wrapping tensors.

    Each sample will be retrieved by indexing tensors along the first dimension.

    Arguments:
        *tensors (Tensor): tensors that have the same size of the first dimension.
    """

    def __init__(self, *tensors):
        assert all(tensors[0].size(0) == tensor.size(0) for tensor in tensors)
        self.tensors = tensors

    def __getitem__(self, index):
        return tuple(tensor[index] for tensor in self.tensors)

    def __len__(self):
        return self.tensors[0].size(0)

import torch
from torch.utils.data import Dataset, DataLoader, TensorDataset
from torch.autograd import Variable
import numpy as np
import pandas as pd

value_df = pd.read_csv('data1.csv'）
value_array = np.array(value_df)
print("value_array.shape =", value_array.shape)  # (73700, 300)
value_size = value_array.shape[0]  # 73700
train_size = int(0.7*value_size)

train_array = val_array[:train_size]  
train_tensor = torch.tensor(train_array, dtype=torch.float32).to(device)
train_label_array = val_array[60:train_size+60]
train_labels_tensor = torch.tensor(train_label_array,dtype=torch.float32).to(device)

train_dataset = TensorDataset(train_tensor, train_labels_tensor)
train_loader = DataLoader(dataset=train_dataset,
                          batch_size=100,
                          shuffle=False,
                          num_workers=0)

for epoch in range(2):
    for i, data in enumerate(train_loader):
        inputs, labels = data
        inputs, labels = Variable(inputs), Variable(labels)
        print(epoch, i, "inputs", inputs.data.size(), "labels", labels.data.size())